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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Muthulingam, Gurusigaamani Ayyanar Dhasmana, Gulshan Purushothaman, S. Honnaiah, Sharath Ronald, Bindu Soudagar, Manzoore Elahi M. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Sathyabama Institute of Science and Technology Department of Electronics and Communication Tamil Nadu Chennai India Credit Acceptance Karnataka Bangalore India Vimannagar Maharashtra Pune411014 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Ensuring data security against unauthorized access in smart homes is crucial, but implementing deep learning methods can be challenging due to potential issues such as accuracy loss, time complexity, and increased error rates. This study introduces a novel approach to address these challenges: the Securing Intelligent Home with a Transformer-Based IoT Intrusion Detection System (IDS) using an Optimized Cross-Contextual Point Running City Game Transformer Network (O2CPRCG-TransNet). This innovative method leverages advanced DL approaches to improve the security and reliability of Intelligent home environments. The study utilizes the IoT-bot dataset, which is pre-processed using the Grid-Constrained Data Purification Method (GCDP) to ensure high-quality input. The purified data is then optimized through the Giant Trevally Optimizer Algorithm (GTOA), which selects the most significant features to improve classification performance. Classification is performed using the O2CPRCG-TransNet architecture, which is specifically designed to address the complexities of Intelligent home security. To ensure secure data transmission, the system incorporates Somewhat Homomorphic Fuzzy-based Elliptic Curve Cryptography (SWH-FECC). Experimental results, obtained using the Python platform, demonstrate that the proposed approach achieves superior performance, with an accuracy of 98% and a recall of 0.993, compared to existing methods. These findings underscore the effectiveness of the proposed method and its potential for broader application in enhancing Intelligent home security. © 2024 IEEE.

关键词： Smart homes

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Impact of dissolution and precipitation on pore structure in CO_(2)sequestration within tight sandstone reservoirs

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Petroleum science 2025年第2期22卷 868-883页

作者： Hui Gao Kai-Qing Luo Chen Wang Teng Li Zhi-Lin Cheng Liang-Bin Dou Kai Zhao Nan Zhang Yue-Liang Liu School of Petroleum Engineering Xi'an Shiyou UniversityXi'an710065ShaanxiChina Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil&Gas Reservoirs in West China Ministry of EducationXi'an710065ShaanxiChina Xi'an Key Laboratory of Tight Oil(shale Oil)Development Xi'an710065ShaanxiChina Department of Electrical Engineering and Computer Science University of StavangerStavanger4036Norway School of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China

Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration *** investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after ***,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is *** quantifies CO_(2)needed for mineralization of soluble ***,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage ***,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller *** pressure levels rise,the magnitude of pore alterations progressively *** a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small *** observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.

关键词： Dissolution Precipitation Pore structure CO_(2)sequestration Unconventional reservoirs

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

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Neural Networks 2023年 161卷 83-92页

作者： Lin, Jiun-Da Han, Yue-Hua Huang, Po-Han Tan, Julianne Chen, Jun-Cheng Tanveer, M. Hua, Kai-Lung Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Mathematics Indian Institute of Technology Indore Simrol453552 India

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods. © 2023 Elsevier Ltd

关键词： Large dataset

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Fast and Accurate Interrogation of Vernier Effect-based Optical Fiber Sensors Enabled by Machine Learning

Fast and Accurate Interrogation of Vernier Effect-based Opti...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhu, Chen Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

The possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (~166 pm) for the interrogation of a Vernier effect-based high-sensitivity optical fiber senso... 详细信息

关键词： Spectrometers

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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D-SVM over networked systems with non-ideal linking conditions

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Iran Journal of computer science 2023年第4期6卷 375-386页

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Zarrabi, Houman Faculty of Mechanical Engineering Semnan University Semnan Iran Electrical Engineering and Computer Science Department Oregon State University Corvallis OR United States Iran Telecom Research Center (ITRC) Tehran Iran

This paper considers distributed optimization algorithms, with application in binary classification via distributed support vector machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions. Logarithmic quantization and clipping (saturation) are two examples of such nonlinearities. In contrast to existing literature that mostly considers ideal links and perfect information exchange over linear channels, we show how general sector-bounded models affect the convergence to the optimizer (i.e. the SVM classifier) over dynamic balanced directed networks. In general, any odd sector-bounded nonlinear mapping can be applied to our dynamics. The main challenge is to show that the proposed system dynamics always have one zero eigenvalue (associated with the consensus) and the other eigenvalues all have negative real parts. This is done by recalling arguments from matrix perturbation theory. Then, the solution is shown to converge to the agreement state under certain conditions. For example, the gradient tracking (GT) step size is tighter than the linear case by factors related to the upper/lower sector bounds. To the best of our knowledge, no existing work in distributed optimization and learning literature considers non-ideal link conditions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Clipping Distributed optimization Log quantization Matrix perturbation theory SVM

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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

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8-HQCdCl_(2)H_(2)O as an organic Q-switcher in erbium laser cavity

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Optoelectronics Letters 2023年第11期19卷 681-685页

作者： MUSTAFA MOHAMMED NAJM MOHAMMED NAJM ABDULLAH AYA A.ALMUKHTAR BELAL AHMED HAMIDA MOH YASIN SULAIMAN WADI HARUN Department of Electrical Engineering Universiti MalayaKuala Lumpur50603Malaysia Department of Computer Engineering University of TechnologyBaghdadIraq Medical Instrumentation Engineering Department Al-Esraa University CollegeBaghdadIraq Department of Electrical and Computer Engineering International Islamic University MalaysiaGombakMalaysia Department of Physics Faculty of Science and TechnologyAirlangga UniversitySurabayaIndonesia Research Center for Biophotonics(RCB) Airlangga UniversitySurabaya60115Indonesia

This paper demonstrated a Q-switched erbium-doped fiber laser(EDFL)using an organic saturable absorber(SA)based on 8-HQCdCl_(2)H_(2)O *** organic thin film was prepared using the casting *** proposed Q-switched EDFL has a maximum repetition rate of 143 kHz,minimum pulse duration of 1.85μs and the highest pulse energy of 167 *** Q-switched peak laser was at a central wavelength of 1531 nm with a 3 dB bandwidth of 3.52 nm and power intensity of 2.64 dBm.

关键词： switched laser material

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Self-Supervise Reinforcement Learning Method for Vacant Parking Space Detection Based on Task Consistency and Corrupted Rewards

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IEEE Transactions on Intelligent Transportation Systems 2024年第2期25卷 1346-1363页

作者： Nguyen, Manh-Hung Chao, Tzu-Yin Hsiao, Ching-Chun Li, Yung-Hui Huang, Ching-Chun Hcm University of Technology and Education Faculty of Electrical Electronic Engineering Ho Chi Minh City700000 Viet Nam National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City114699 Taiwan

This paper proposes a novel task-consistency learning method that enables us to train a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a parking lot. Note that the source task can introduce false detection during task-consistency learning, which implies noisy rewards or supervision. The target network can be trained in a reinforcement learning setting by appropriately designing the reward mechanism upon semantic consistency. We also introduce a novel symmetric constraint to detect corrupted samples and reduce the effect of noisy rewards. Unlike conventional corrupted learning methods that use only training losses to identify corrupted samples, our symmetric constraint also explores the relationship among training samples to improve performance. Compared with conventional supervised detection methods, the main contribution of our work is the ability to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property allows the proposed detector to be easily deployed and updated in various lots without heavy human loads. Experiments demonstrate that our noisy task consistency mechanism can be successfully applied to train a vacant space detector from scratch. © 2000-2011 IEEE.

关键词： Semantics

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